A High-Performance Deep Neural Network Model for BI-RADS Classification of Screening Mammography

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 3; p. 1160
• Main Authors: Tsai, Kuen-Jang, Chou, Mei-Chun, Li, Hao-Ming, Liu, Shin-Tso, Hsu, Jung-Hsiu, Yeh, Wei-Cheng, Hung, Chao-Ming, Yeh, Cheng-Yu, Hwang, Shaw-Hwa
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 03.02.2022; MDPI
• Subjects: Aged; Annotations; Area Under Curve; Breast cancer; breast imaging reporting and data system (BI-RADS); Breast Neoplasms - diagnostic imaging; Calcification; Cancer; Care and treatment; Classification; Datasets; Deep learning; deep neural network (DNN); Diagnosis; Early Detection of Cancer; Female; Humans; image classification; Mammography; Medical screening; Middle Aged; Neural networks; Neural Networks, Computer; Political aspects; Rankings; screening mammography; Taiwan; breast imaging reporting and data system (BI-RADS); deep learning; screening mammography; image classification; deep neural network (DNN)

Globally, the incidence rate for breast cancer ranks first. Treatment for early-stage breast cancer is highly cost effective. Five-year survival rate for stage 0–2 breast cancer exceeds 90%. Screening mammography has been acknowledged as the most reliable way to diagnose breast cancer at an early st...